Extending open source and hybrid cloud to drive OT transformation - Future Oil & Gas conference

Editor's Notes

• #6: If Scope 3 emissions have gained such attention, it is partly due to its massive downstream impact in the Automotive and the Oil & Gas industries. In the Automotive sector, Scope 3 emissions have a considerable influence, as it accounts for 95% of the total induced emissions. In the Oil & Gas industry, Scope 3 alone represents 85% of emissions [1] of the industry. Shell, for instance, has 90% of its emissions stemming from its supply chain and the use of its products.
• #7: Is your Data Trustworthy? Need to understand the ‘lineage’ of the data. You need to ‘label’ your data so that you know which data you have used For warehouse and data lakes you need to keep the timeliness of data in mind Data Gravity - Some countries will not let data out of the country (need Hybrid solution)
• #9: Intro - Business innovation is driven by big idea: Wow - what an incredible time we live in. What an exciting time to be alive! Business is moving faster than ever before. Today, we can do things we could only dream of a few years ago. Technology, open source communities and new ways of collaboration are driving business innovation No longer are we looking at startups and Web 2.0 companies like Facebook, Uber and Airbnb for inspiration as to what innovation looks like. Today, every organization in every geography and any industry can innovate, create more customer value and differentiation and compete on an equal playing field. And with Red Hat OpenShift, we’re building on our heritage of Red Hat Enterprise Linux to provide you with a platform that enables your organization to innovate faster. But why is being able to move faster and innovate so important?
• #10: So the question comes back to you… Do you need to deliver solutions faster? Will delivering solutions faster help your organization innovate and exceed its goals? <Let customer talk>
• #11: See also here: https://docs.google.com/presentation/d/1kCQJs0GaYFvmQv1RPov8yUEeNmyfE0tDUc2Aj2hZ8AU/edit#slide=id.gd93df9f22e_0_0
• #12: Data ecosystems are becoming more complex, especially as cloud-based data platforms are added to the mix This means that the process by which the business gets answers to its questions is also becoming more complex In a modern data ecosystem, there is massive amounts of data sitting in a variety of locations and formats, like database, data lakes and warehouses, both on prem and in the cloud. Worse yet, this data can be silod. Adding to the silos, there could be a lack of cross-team collaboration For example, NOC assets may be looked after by different groups, and are therefore stored in different locations. You may need to obtain permission to access the data you are interested in and services of that team’s SME for help in obtaining the data you want. When data is pulled out of a silo, Legacy tech and poor automation may cause error prone data. In order to effectively analyze the data, it needs to put it in a common format automatically and be subject to Data Governance: Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused. It's increasingly critical as organizations face new data privacy regulations and rely more and more on data analytics to help optimize operations and drive business decision-making. This requires you to consolidate it into a single location by moving and/or copying it into that location. (address why consolidating the data into a single location may not be a good idea, in next slide) All these steps are extremely time consuming, it can get quite expensive, and it adds 0 value while also increasing security risks by having multiple copies of your data laying around. Ultimately these steps drive down your ability to provide insights at the speed of business. It is no surprise that 80% of an enterprise’s time is spent on making the data available for analysis - while 20% is spent on finding the answers to their questions Let’s address why consolidating the data into a single location is not a good idea
• #13: Consolidation is extremely time consuming and isn’t sufficient Silos still exist Data Warehouses & Lakes leave data in original form. Little change in time to result Still need SMEs, manual process, no governance, etc. Consumers handle data preparation Data consumers still responsible for transformations This can be dangerous, as customers may choose metric system instead of an imperial system for the data. If the organization uses imperial system and bases its calculations for volumes then costly errors will be made when the customers go ahead and use their model/analytics on other data sets within the organization. Lacking business-based data model At the end of the day, the Data should be transformed into the form the business needs and understands. Requiring automation forces global understanding as teams now self-service based on their needs All these items seem to point that getting good data in a timely manner is impossible. It’s not, because the current time we have is right for changing the way we store/access/gather and prepare data due to a number of factors: Cloud computing (public & onPrem) Usage of Open source technologies such as containers Edge computing Programming capabilities increased Standards adoption Most important of all we have O&G C-suite level acceptance With that let’s look at how we can consume data using some of these factors
• #15: Enterprise Edge: horizontal stuff, usable from IT / OT, not specific to Operations Edge: vertical, OT specific, Industrial specific Provider Edge: Telco Specific, Private 5G solutions Vehicle Edge: rather Automotive specific, more details here: More detail on Vehicle edge: https://docs.google.com/presentation/d/1Fc4-bWCxsSxAG8DWs18T57sm-KA0qrlRDCrYHslp6TE/edit#slide=id.gee4169c65d_0_110
• #16: Single Node now joins our previously announced 3 Node clusters and Remote worker nodes. 3 Node clusters for sites that require high availability, but in a smaller, 3 node footprint Remote worker nodes where only worker nodes are in smaller edge locations while the controller nodes are in larger sites like regional data centers Single node, which will provide both software high availability* and a smaller footprint - this is currently scheduled to be available in the second half of 2021 * if a container fails, kubernetes is able to restart it. Obviously hardware failures are not covered when you are running on a single server.if a container fails, k8s will relaunch itif a container fails, k8s will relaunch it
• #19: And Red Hat and its system integrator partners are there to help you on every step of the journey, from the culture change and skills development needed to move to cloud native development, to the modernization of existing applications to containers, to optimizing processes for developers and IT
• #20: Challenges Data science is transformational, but its potential is being limited by five key things. It we solve these things, we can get better insights that translate into business value. Organizations now have access to huge amounts of data, and it is growing exponentially. There is so much data that it’s next to impossible to process all of it. Making matters words, it’s inconsistent--it’s from different sources, different time periods, in different formats. The end of Moore’s law means that CPUs aren’t just automatically getting significantly faster year after year. Popular data science tools are CPU-constrained, making users sit through long periods of processing time. This is exasperated by the flood of incoming data making data sets larger than ever. The popular data science tools are spread out among dozens of software repositories, many of them are open source and revised frequently, and it’s very challenging to find the right versions that will all work together.
• #23: The next disruptive evolution in technology is not about new companies disrupting traditional incumbents—it’s about traditional incumbents in “old fashioned industries” using technology to connect their preexisting infrastructures to create increased efficiency. Red Hat has traditionally served IT organizations and the journey that they have been on for the last decade-plus as software-defined platforms have become prevalent is now coming to OT, which opens up even more potential value, as planning and execution systems converge and formerly closed systems get replaced by open architectures designed to support data-driven insights.
• #25: Red Hat’s edge computing Validated Patterns are repositories of configuration templates in the form of Kubernetes manifests that describe an edge computing stack fully declaratively and comprehensively; from its services down to the supporting infrastructure. Validated Patterns facilitate complex, highly reproducible deployments and are ideal for operating these deployments at scale using GitOps operational practices. Use a GitOps model to deliver the Pattern as code Use as a POC, modified to fit a particular need that you can evolve into a real deployment. Highly reproducible - great for operating at scale Open for collaboration, so anyone can suggest improvements, contribute to them